GLEm-Net: Unified Framework for Data Reduction with Categorical and Numerical Features

In the era of Big Data, effective data reduction through feature selection is of paramount importance for machine learning. This paper presents GLEm-Net (Grouped Lasso with Embeddings Network), a novel neural framework that seamlessly processes both categorical and numerical features to reduce the dimensionality of data while retaining as much information as possible. By integrating embedding layers, GLEm-Net effectively manages categorical features with high cardinality and compresses their information in a less dimensional space. By using a grouped Lasso penalty function in its architecture, GLEm-Net simultaneously processes categorical and numerical data, efficiently reducing high-dimensional data while preserving the essential information. We test GLEm-Net with a real-world application in an industrial environment where 6 million records exist and each is described by a mixture of 19 numerical and 7 categorical features with a strong class imbalance. A comparative analysis using state-of-the-art methods shows that despite the difficulty of building a high-performance model, GLEm-Net outperforms the other methods in both feature selection and classification, with a better balance in the selection of both numerical and categorical features

Phosphoinositide 3-kinase gamma gene knockout impairs postischemic neovascularization and endothelial progenitor cell functions

OBJECTIVE: We evaluated whether phosphatidylinositol 3-kinase γ (PI3Kγ) plays a role in reparative neovascularization and endothelial progenitor cell (EPC) function. METHODS AND RESULTS: Unilateral limb ischemia was induced in mice lacking the PI3Kγ gene (PI3Kγ(−/−)) or expressing a catalytically inactive mutant (PI3Kγ(KD/KD)) and wild-type controls (WT). Capillarization and arteriogenesis were reduced in PI3Kγ(−/−) ischemic muscles resulting in delayed reperfusion compared with WT, whereas reparative neovascularization was preserved in PI3Kγ(KD/KD). In PI3Kγ(−/−) muscles, endothelial cell proliferation was reduced, apoptosis was increased, and interstitial space was infiltrated with leukocytes but lacked cKit(+) progenitor cells that in WT muscles typically surrounded arterioles. PI3Kγ is constitutively expressed by WT EPCs, with expression levels being upregulated by hypoxia. PI3Kγ(−/−) EPCs showed a defect in proliferation, survival, integration into endothelial networks, and migration toward SDF-1. The dysfunctional phenotype was associated with nuclear constraining of FOXO1, reduced Akt and eNOS phosphorylation, and decreased nitric oxide (NO) production. Pretreatment with an NO donor corrected the migratory defect of PI3Kγ(−/−) EPCs. PI3Kγ(KD/KD) EPCs showed reduced Akt phosphorylation, but constitutive activation of eNOS and preserved proliferation, survival, and migration. CONCLUSIONS: We newly demonstrated that PI3Kγ modulates angiogenesis, arteriogenesis, and vasculogenesis by mechanisms independent from its kinase activity

Integrated Sensors & Read-Out: simulations, design and tests for highly advanced applications, from Robotics to High Energy Physics

This thesis presents design, simulations and tests of Silicon CMOS and Bi-CMOS sensors and read-out circuits for robotic and high energy physics applications. The project is divided in two main sub-parts: • Design, analysis, simulations, layout and tests of integrated sensors and electronic read-out circuits. Integrated temperature sensor and reference were designed for biomedical applications, while two read-out circuits were conceived to interface pressure sensors in robotic applications, more specifically hand exoskeletons and robotic tactile skin. • Physics performance studies of the Pixel detectors of the ATLAS experiment at CERN, as an example of advanced silicon device for ionizing particle detection. The fist Chapters of this thesis intend to show that even if most robotic applications prefer to rely on commercially available ICs to read-out tactile information, a custom solution can be even more effective as it offers some crucial advantages. For instance, human response to touch has a complex dynamics which is very difficult to be faithfully replicated at robotic-level with a general-purpose block, such as a microcontroller. On the contrary, custom solutions offer a much higher flexibility to reproduce human physiology at robotic level, without compromising the system modularity. The presented read-out circuits feature the crucial advantage of tunable output sensitivity, low-power consumption and compact size. Furthermore,they are designed with logic blocks only, to be modularly replicated and possibly implemented in different technologies with low efforts. The analog-to-digital conversion was achieved through a voltage-to-frequency conversion, which is an effective technique to partially attenuate the dramatic consequences of scaling in purely analog design. The read-out circuit for tactile skin has the major advantage of robustness with respect to process, voltage and temperature variations, which makes the solution even more attractive for the read-out of robotic systems. The interface for hand exoskeleton presents the important feature of providing both contact detection and pressure evaluation. The thesis details all steps carried out by the candidate, from the transistor-level design, through the simulations and layout to the electrical measurements, which allow to test the performance of the read-out circuit couple with the sensor. The temperature sensor and reference were designed in Bi-CMOS technology and exploited a bandgap architecture to take advantage of the linear dependence of bipolar transistor base-emitter voltage with absolute temperature. These ICs are still under development, hence only design and simulations are provided. Nevertheless, knowledge gained from the analog design (e.g., temperature dependence of integrated capacitors) was later used for the performance analysis of the pixel sensor of the IBL sub-detector. Experience achieved from design of integrated sensors and electronic interfaces offers peculiar expertise to analyze the performance of pixel sensors for particle Physics applications in terms of charge collection properties. The pixel integrated sensor and read-out circuit was studied in detail thanks to the expertise gained by designing the previously presented ICs. Specifically, it was demonstrated that effects in the read-out system (e.g., quantization resolution, electronic noise, radiation damage of the sensitive substrate) play an important role in defining the sensor performance, hence dramatically affect the tracking performance of the overall LHC. The pixel performance was studied in terms of charge calibration, spatial resolution optimization and two track separation. This analysis was performed using both custom simulations of charge collection phenomena (occurring at the sensor and read-out level) and official ATLAS Monte Carlo simulation software. The results were successfully compared to ATLAS collision data and result of dedicated Beam Test